Artificial intelligence: the devil is in the data

Source: Sponsored interview

It’s no secret that the public sector and its service providers need to invest in technology to help make better use of their resources. But is it really just as simple as buying new software? Simon Dennis, director of artificial intelligence (AI) and analytics innovation at SAS UK, tells PSE’s Luana Salles where the power of digital truly lies.

“Austerity has bit as hard as it can,” Simon Dennis, director of AI and analytics innovation at SAS UK, tells me. “We’ve now gone slightly beyond where we probably should have done in terms of cutting spending. The only way that will be addressed is if, across the whole of the public sector, people join up so that you can find the best, most efficient way of delivering services. Learning from each other is vital, and I think we’ll see a lot more of that coming as the devolution agenda grows.”

He’s not wrong, either: local authorities up and down the country are approving sizeable budget savings to deal with their finite resources, while government departments are operating within considerably tighter limits. Much is said about the need to break down siloes in the public sector and ensure organisations speak to each other, but what does this mean in the context of the digital age?

In one word, analytics. “You need data to be managed, shared appropriately, and then analysed,” explained Simon. “Analytics is what AI is at the moment; what gives them intelligence is learning from the past. Machines are using their ability to visualise all the data available in order to make a better decision than we could, because we could never get our head around it all.”

For the public sector, this could be deployed across several sectors. SAS, for example – the world’s largest privately-held software business – works with public providers across a number of areas, ranging from debt, fraud, and crime to healthcare and local government. For councils, intelligent analytics could help with better citizen engagement, more accurate economic projections, a clearer picture of criminal activity, or even smarter cities. For central government, it can help with better tax and revenue management, more efficient debt collection and prevention, or more streamlined procurement.

Take, for instance, local roads: machines can know exactly how many council staff are filling potholes, where they are working, and where the problem spots are. Having an employee analyse who to send to a job – based on where they currently are and how long it should take them to carry out different tasks, including journey times – would be nearly impossible. For a machine, it’s easily done.

“When you see a specific scenario happening, the machines can also know what has been done in the past to counter it and, with the resources available, what is the most efficient way of tackling it at present,” added Dennis.

The good, the bad, and the budget

There are, however, a few things holding the public sector back from investing in this. The first is mentality: organisations need to undertake some due diligence before deciding whether or not to invest in AI. In a chicken-or-egg scenario, what comes first is the problem at hand; only after pinpointing this can you then think about whether AI can help solve it, and whether the outcomes will be worth the development costs.

To support this, research is vital. According to a recent study by SAS and partners, the top benefits of AI include more accurate forecasting and decision-making, better customer acquisition, higher productivity, and reduced manual tasks. Using this information as guidance can help determine whether a strategic objective could be assisted by the right piece of tech.

Secondly, there’s the stigma. You’ve heard it all before: automation will take away jobs and risk will become the new normal. To dispel these myths, the public sector must make it crystal-clear that AI is there to facilitate complex processes. As Simon explained: “The machine will make jobs easier and take away some of the drudgery. People will get different roles.”

The debate around ethical AI also extends to data bias or, in other words, information previously affected by human bias. To combat this, government must take steps to ensure that the data fed into the AI is as unbiased as possible, as well as address the issue of historical bias so that citizens can be confident that all decisions are being made ethically. It’s no small feat, but one better tackled now than dealt with later.

Last but not least, there’s the all-important issue of money. Resources are limited, and the public sector can’t always afford the ‘spend now to save later’ mentality. But according to Dennis, the private sector is willing to step up to the mark. For instance, SAS has closed commercial deals where it agrees to carry out a programme and then later takes a percentage of the profit.

“We know from our experience elsewhere that it’s going to turn around very quickly, and we’re going to make money in the first year,” he told me. “I have never been involved in a project where we’ve done a proof of concept to show something is possible, but then it hasn’t worked – and I’ve been doing this for 20 years now. The devil, though, is in the data: if you get the data right, then it’s not a risky enterprise.”

To ensure programmes will succeed, it’s all down to contained experimentation – try something out, and if it’s not working or it’s too risky, stop doing it.

When to ask for help

Of course, another sticking point is the public sector’s lack of expertise in the field, and a resulting lack of experience in marrying the AI element to the human element. Bringing in a private company as a partner to help define these boundaries and break new ground is the most obvious solution, but organisations should be cautious: a lot of companies may claim to have AI skills, but only a handful of key suppliers actually know what they’re talking about.

SAS is part of the latter group: with 40 years of experience in the field, the company has developed a wide range of technology designed to suit any need. Even more importantly, it knows what does and doesn’t work. “It’s the analytical capabilities that go into understanding your data that make AI look smart – so getting those analytics right, which is what we’ve been doing for 40 years, is critical,” noted Simon.

“The flexibility, too, is key. You need to have an entrepreneurial environment where data scientists are free to be innovative and try new things, and then you need to have a more productionised side with strong governance and security. We are, without a doubt, world leaders in terms of being able to provide that. You can have something that is proven and solid to actually control and operate going forward, with the widest range of algorithmic development capabilities in the world.”

While SAS’s track record in the UK public sector speaks for itself – having worked with HMRC on debt management, the London Fire Brigade on fire prevention, and even on the Treasury’s yearly Budget – its portfolio stretches far beyond that. Simon had a few stories to share, but the most interesting one was his company’s monitoring of wildlife to help protect animals from extinction. AI can use visual recognition to work out which footprint belongs to which animal in order to understand migration paths across Africa, whereas previously there was a lot of human intervention involved.

The UK public sector doesn’t yet have to deal with endangered rhinos, of course, but just as worrying are the threatened public services at risk of shutting down altogether without a new approach. With some cash-strapped councils already starting to consider delivering only a bare-bones ‘core offer’ to residents in face of narrow resources, it’s time we step outside our comfort zone and explore the fresh possibilities of data automation.